15.3 Digital elevation models and terrain analysis
6 min read•july 30, 2024
Digital elevation models (DEMs) are powerful tools for analyzing Earth's surface. They create 3D terrain representations, enabling geomorphologists to study landforms, quantify processes, and identify hazards across large areas. DEMs extract crucial topographic attributes like and .
applications in geomorphology include delineation, , and tectonic studies. While incredibly useful, DEMs have limitations such as potential errors from data collection and . Understanding these constraints is key to effectively using DEMs in landscape analysis.
Digital Elevation Models for Geomorphology
Fundamentals and Applications of DEMs
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- Digital Elevation Models (DEMs) create 3D terrain surface representations stored as raster grids with elevation values for each cell
- DEMs provide continuous topography coverage enabling quantitative analysis of landscape features and processes across large areas
- DEM resolution affects terrain feature representation (higher resolutions capture finer details but require more data storage and processing power)
- Geomorphologists utilize DEMs to study landform evolution, quantify and deposition rates, and identify geohazard-susceptible areas
- DEMs extract crucial topographic attributes for understanding surface processes and landform development
- Slope
- Curvature
DEM Applications in Geomorphology
- Watershed delineation determines drainage basin boundaries and stream networks
- Slope stability analysis identifies areas prone to landslides or mass wasting
- Flood modeling simulates inundation extents and depths for various flood scenarios
- Landform classification automatically categorizes terrain into distinct geomorphic units (ridges, valleys, plains)
- Sediment transport modeling estimates erosion rates and sediment flux across landscapes
- Tectonic geomorphology studies landscape response to active tectonics and uplift
Limitations and Considerations
- Potential errors arise from data collection methods (instrument accuracy, point density)
- Interpolation techniques can introduce artifacts or smooth out important features
- DEMs cannot represent subsurface features or fully capture vegetation canopy structure
- Temporal resolution limits ability to capture rapid landscape changes (landslides, volcanic eruptions)
- Vertical accuracy varies depending on terrain type and data collection method
- Edge effects can occur at the boundaries of DEM datasets, requiring careful merging techniques
DEM Generation and Processing
Data Acquisition Methods
- (Light Detection and Ranging) provides high-resolution elevation data
- Laser pulses penetrate vegetation to create bare-earth models
- Capable of sub-meter vertical accuracy in ideal conditions
- Photogrammetry techniques construct DEMs through image correlation and triangulation
- Structure from Motion (SfM) uses overlapping aerial or drone imagery
- Satellite stereo imagery enables global-scale DEM generation (ASTER GDEM, SRTM)
- Interferometric Synthetic Aperture Radar (InSAR) generates DEMs by measuring radar signal phase differences
- Useful for large-scale mapping and detecting surface deformation
- Can operate through cloud cover and at night
- Traditional field surveying methods provide accurate point data for smaller study areas
- Total station measurements
- Differential GPS surveys
- Terrestrial laser scanning for high-resolution local DEMs
DEM Creation Process
- Data preprocessing prepares raw elevation data for interpolation
- Noise removal filters out erroneous points or outliers
- Georeferencing aligns data to a common coordinate system
- Point classification separates ground returns from vegetation or buildings (for LiDAR)
- Interpolation creates a continuous surface from discrete elevation points
- (IDW) uses nearby points weighted by distance
- applies geostatistical methods to estimate optimal interpolation
- Triangulated Irregular Network (TIN) creates a mesh of triangles from input points
- Resampling adjusts DEM resolution to match project requirements or computational constraints
- Bilinear interpolation for smoother transitions between cells
- Nearest neighbor resampling preserves original cell values
Quality Assessment and Error Correction
- Identify and mitigate artifacts, voids, and systematic errors in DEMs
- Visual inspection using hillshade and contour maps
- Statistical analysis of elevation distributions and derivatives
- Comparison with reference data or higher-accuracy DEMs
- Common DEM errors require specific correction techniques
- Stripe removal for sensor-related artifacts
- Void filling using interpolation or auxiliary data sources
- Hydrological correction ensures proper flow routing across the landscape
- Uncertainty assessment quantifies DEM quality and limitations
- Error propagation analysis for derived products
- Monte Carlo simulations to model impact of DEM uncertainty on analyses
Terrain Analysis with DEMs
Topographic Attribute Extraction
- Slope analysis quantifies terrain steepness and orientation
- Calculated using finite difference or polynomial fitting methods
- Critical for understanding erosion, mass wasting, and hydrological flow
- Typically expressed in degrees or percent rise
- Aspect calculation determines compass direction of slope faces
- Influences factors like solar radiation receipt and vegetation distribution
- Often represented using cardinal or intercardinal directions
- Curvature analysis identifies convex and concave landforms
- Profile curvature measures curvature parallel to the slope
- Plan curvature measures curvature perpendicular to the slope
- Helps delineate ridges, valleys, and areas of potential erosion or deposition
Hydrological Modeling
- algorithms determine paths of water movement across the landscape
- D8 algorithm assigns flow to one of eight neighboring cells
- Multiple flow direction algorithms allow for flow divergence
- calculates upstream contributing area for each cell
- Essential for stream network delineation and watershed analysis
- Often used to define channel initiation thresholds
- (TWI) predicts areas of soil moisture accumulation
- Combines slope and flow accumulation:
- Where a is the upslope contributing area and β is the local slope angle
- Higher values indicate greater potential for saturation
Advanced Terrain Analysis
- quantify terrain complexity
- (VRM) uses 3D vector dispersion
- (TRI) calculates elevation differences between cells
- Useful for identifying geomorphic features and assessing landscape heterogeneity
- Geomorphon classification automatically identifies and maps landforms
- Uses pattern recognition based on local geometry and landscape context
- Typically classifies terrain into 10 common landform types (peak, ridge, slope, etc.)
- (TPI) compares elevation of each cell to mean elevation of neighborhood
- Positive values indicate local ridges or peaks
- Negative values indicate local valleys or depressions
- Used for landform classification and habitat modeling
Interpreting DEM-Derived Products
Visualization Techniques
- Hillshade rendering creates 3D-like terrain representation
- Simulates illumination to enhance visual interpretation of landforms
- Adjustable light source angle and elevation for optimal feature highlighting
- Contour maps provide traditional 2D elevation representation
- Useful for identifying gradients and landform boundaries
- Contour interval selection balances detail and map readability
- 3D visualization techniques enhance landscape feature interpretation
- Draping satellite imagery or thematic maps over DEMs
- Virtual fly-throughs for immersive landscape exploration
- Augmented reality applications for field-based visualization
Quantitative Landscape Analysis
- uses elevation distribution to infer geomorphic development
- Hypsometric curve plots cumulative area against relative elevation
- Hypsometric integral quantifies overall landscape convexity or concavity
- Indicates stages of landscape evolution and tectonic influence
- Cross-sectional profiles reveal vertical relationships between landforms
- Useful for identifying erosional or depositional signatures
- Can be stacked to create swath profiles for broader landscape characterization
- Slope-area analysis examines relationship between drainage area and local slope
- Helps identify process domains in fluvial landscapes (hillslopes vs. channels)
- Used to estimate channel concavity and steepness indices
Multi-temporal and Integrated Analysis
- Change detection analysis quantifies surface changes over time
- Requires multi-temporal DEMs of consistent resolution and accuracy
- Applications include landslide volume estimation, glacier mass balance, and coastal erosion monitoring
- calculates elevation changes between two time periods
- Produces maps of erosion and deposition
- Requires careful error propagation and significance testing
- Integration of DEM-derived products with other spatial data in
- Combining slope and geology maps to assess landslide susceptibility
- Merging land cover data with TWI to model habitat suitability
- Incorporating climate data with terrain attributes for soil erosion modeling
Key Terms to Review (27)
ArcGIS: ArcGIS is a powerful geographic information system (GIS) software suite used for creating, analyzing, and sharing spatial data. It allows users to visualize and interpret complex geographic information, making it essential for landscape analysis techniques, GIS applications, digital elevation models, and emerging technologies in geomorphological research. By providing tools for mapping, spatial analysis, and data management, ArcGIS supports decision-making in environmental planning and resource management.
Aspect: Aspect refers to the compass direction that a slope faces, which is crucial for understanding various landscape features and processes. This direction influences microclimates, vegetation patterns, and erosion rates, as slopes facing different directions can experience varying levels of sunlight and moisture. Consequently, aspect plays a significant role in landscape analysis techniques, slope stability assessment, and the creation of digital elevation models.
Contour mapping: Contour mapping is a graphical representation that uses contour lines to connect points of equal elevation on a surface, effectively illustrating the terrain's shape and features. This technique is essential in visualizing topography and helps in analyzing landforms, slopes, and drainage patterns, making it a crucial tool in fields like geology, geography, and environmental science.
Curvature: Curvature refers to the amount by which a geometric object deviates from being flat or straight. In the context of terrain analysis and digital elevation models, curvature is essential for understanding how the shape of the land influences various surface processes, including water flow, erosion, and habitat distribution. It helps in identifying concave or convex features in the landscape, which are critical for modeling and analyzing physical processes occurring on the Earth's surface.
DEM: A Digital Elevation Model (DEM) is a 3D representation of a terrain's surface, created from terrain elevation data. DEMs are used to analyze the earth's surface and its features, providing crucial information for various applications such as topographic mapping, hydrological modeling, and landscape visualization. They serve as essential tools in understanding how terrain influences both natural processes and human activities.
Dem differencing: Dem differencing refers to the process of subtracting one digital elevation model (DEM) from another to identify changes in terrain over time. This method is commonly used in terrain analysis to detect alterations caused by natural events like erosion, landslides, or human activities such as construction. By analyzing the differences between two DEMs, researchers can assess landscape dynamics and monitor environmental changes effectively.
Erosion: Erosion is the process by which soil, rock, and other surface materials are worn away and removed from their original location by natural forces such as water, wind, ice, or gravity. This process is essential in shaping landscapes and influencing sediment transport, which connects various components of the Earth's surface system.
Flood modeling: Flood modeling is the process of simulating and predicting flood events using various techniques and tools to understand their potential impact on the environment, infrastructure, and communities. It incorporates digital elevation models (DEMs) and terrain analysis to assess how water moves across landscapes, which is critical for effective flood management and disaster preparedness.
Flow accumulation: Flow accumulation refers to the process of quantifying how much water flows over a surface area and accumulates in specific locations, often used in digital elevation models to analyze terrain and hydrology. This concept is essential for understanding drainage patterns, predicting flood risks, and managing water resources effectively. By analyzing flow accumulation, researchers can identify where water is likely to converge and influence landscape features, which has implications for both natural ecosystems and human infrastructure.
Flow direction: Flow direction refers to the path that water or sediment takes as it moves across a surface, typically influenced by gravity and topography. Understanding flow direction is crucial in terrain analysis as it helps in predicting how water will move through landscapes, which can impact erosion, sediment transport, and the development of drainage networks.
GIS: GIS, or Geographic Information System, is a technology used for capturing, storing, analyzing, and managing spatial and geographic data. It enables users to visualize and interpret data in relation to geographical locations, making it a powerful tool for understanding patterns and relationships in the Earth's surface processes. GIS integrates various types of data, including maps, satellite imagery, and demographic information, allowing for comprehensive analysis and decision-making.
Grid resolution: Grid resolution refers to the size of the cells in a grid used to represent spatial data, such as in digital elevation models (DEMs). The finer the grid resolution, the smaller the cells, allowing for more detailed representation of terrain features and variations. This impacts the accuracy and precision of analysis in terrain studies, influencing decisions in areas such as hydrology, land use planning, and environmental management.
Hillshading: Hillshading is a technique used in geographic information systems (GIS) and cartography to represent the three-dimensional characteristics of terrain on a two-dimensional map. By simulating the effects of light and shadow on the landscape, hillshading enhances the visual interpretation of elevation data, helping to reveal features such as hills, valleys, and slopes. This technique is crucial for effective terrain analysis, making it easier to understand the topography and spatial relationships of the Earth’s surface.
Hypsometric Analysis: Hypsometric analysis is a technique used to study the distribution of elevations within a specific area, allowing researchers to understand the relationship between landforms and elevation. This analysis often involves the use of data visualization methods to represent the elevation profiles and can reveal important insights about landscape processes, geomorphology, and hydrology. By analyzing how much area lies within different elevation ranges, hypsometric analysis provides valuable information for understanding topography and its impacts on various Earth surface processes.
Interpolation: Interpolation is a mathematical technique used to estimate unknown values between two known data points. In the context of digital elevation models and terrain analysis, interpolation helps to create continuous surfaces from discrete elevation data, allowing for more accurate representations of terrain and facilitating analysis of landscape features.
Inverse distance weighting: Inverse distance weighting (IDW) is a spatial interpolation technique used to estimate unknown values at certain locations based on the values from surrounding points, where closer points have more influence on the estimated value than those further away. This method is particularly useful in geographic information systems (GIS) for creating continuous surfaces from discrete data points, enabling more accurate analysis of landscape patterns and terrain features.
Kriging: Kriging is a geostatistical interpolation method used to predict unknown values based on the spatial correlation of known data points. It relies on the concept of spatial autocorrelation, where points that are closer together are more likely to have similar values. This technique is especially useful in fields such as landscape analysis and terrain modeling, as it helps create accurate representations of spatial phenomena based on limited sampling data.
Lidar: Lidar, which stands for Light Detection and Ranging, is a remote sensing technology that uses laser pulses to measure distances and create high-resolution maps of the Earth's surface. This technology is pivotal for analyzing landscapes as it provides detailed elevation data, which is essential for understanding geomorphological processes, terrain features, and spatial patterns in various environments.
Pixel size: Pixel size refers to the dimensions of a single pixel in a digital image or model, typically measured in meters when applied to spatial data. This size influences the level of detail and accuracy in digital elevation models, as smaller pixel sizes can capture finer terrain features while larger pixel sizes may smooth over them. The choice of pixel size is crucial for effective terrain analysis, as it affects the resolution and the ability to derive meaningful topographic information.
Roughness indices: Roughness indices are quantitative measures used to describe the texture and irregularities of a surface, typically in relation to terrain analysis. These indices help to characterize the degree of variability in elevation across a landscape, which can be crucial for understanding surface processes, hydrology, and ecological habitats. By utilizing digital elevation models (DEMs), researchers can derive roughness indices that reflect how uneven or smooth a terrain is, impacting various environmental and engineering applications.
Sedimentation: Sedimentation is the process through which solid particles settle out of a fluid, typically water, and accumulate over time to form sediment layers. This process plays a crucial role in shaping landscapes, influencing river channel patterns, and impacting drainage basin morphology and evolution.
Slope: Slope is a measure of the steepness or incline of a surface, defined mathematically as the change in elevation (rise) over the horizontal distance (run) between two points. This concept is crucial in terrain analysis as it helps in understanding the relationship between landforms and processes like erosion, water flow, and vegetation distribution.
Terrain Ruggedness Index: The Terrain Ruggedness Index (TRI) is a quantitative measure used to assess the complexity and variability of terrain in a given area. It helps in understanding how much a landscape deviates from flatness by calculating the amount of elevation change in relation to the distance between points. This index is vital for analyzing various environmental processes, land use planning, and ecological research.
Topographic Position Index: The topographic position index (TPI) is a numerical measure that assesses the relative elevation of a given point within its surrounding landscape. It helps to identify landforms by comparing the elevation of a point to the average elevation of its neighbors, indicating whether the point is on a ridge, valley, or slope. This analysis is crucial for understanding terrain features and can be effectively calculated using digital elevation models.
Topographic Wetness Index: The Topographic Wetness Index (TWI) is a numerical value that quantifies the potential for soil saturation based on the topography of a given area. It is calculated using digital elevation models to assess how landscape features, like slope and drainage area, influence water accumulation and movement. TWI helps in understanding hydrological processes, including runoff, erosion, and habitat suitability, making it a crucial tool in terrain analysis.
Vector Ruggedness Measure: Vector Ruggedness Measure (VRM) is a quantitative metric used to assess terrain complexity based on the variability in elevation within a digital elevation model (DEM). This measure captures the degree of surface irregularity, which can be essential for understanding ecological patterns, hydrology, and land use. By analyzing changes in slope and aspect, VRM provides insights into how rugged a landscape is, influencing factors like biodiversity and human activity.
Watershed: A watershed is an area of land that drains rainwater or snowmelt into a specific body of water, like a river, lake, or ocean. This concept is crucial for understanding how water moves across landscapes, how different land uses impact water quality, and how natural processes shape drainage patterns over time.